A Resilience-Based Methodology for Improved Water Resources Adaptation Planning under Deep Uncertainty with Real World Application

One of the greatest challenges facing decision makers in the water industry in the UK and worldwide are the increasing influences of “deep” climate change, population growth and urbanization uncertainties affecting the long-term balance of supply and demand and necessitating the need for adaptive action (Environment Agency 2013). Walker et al. (2013) defines the circumstances at which uncertainties can be classified as “deep” as when “one is able to enumerate multiple plausible alternatives without being able to rank the alternatives in terms of perceived likelihood”. Under this definition, which is utilized in this paper, uncertainties are often categorized by the generation of multiple future scenarios to represent a range of “alternative plausible conditions under different assumptions” (Mahmoud et al. 2009). Combining these scenarios with a suitable metric to measure system sensitivity to changing conditions (i.e., robustness) can then facilitate the examination of the potential benefits of alternative system configurations (i.e., adaptation strategies) across a range of deep uncertainties. The interaction of deep uncertainty, scenarios, robustness and adaptation is discussed in detail by Maier et al. (2016).

The complexity of these interactions brings into question the ability of current UK and international engineering planning approaches to deal with deep uncertainties. For example, the current water supply planning approach in the UK is to ensure a regional water system maintains a designated ‘level of service’ to its customers (NERA 2002; Environment Agency et al. 2012). This is essentially an agreement between a water company and its customers describing the average frequency that a company will implement temporary restrictions on water use. However, this ‘level of service’ calculation lacks transparency and is often presented as a general target (e.g., a target system performance of no more than 1 in 10 or 1 in 15 years enforced restrictions (Bristol Water 2014)). It is also calculated irrespective of the duration of each projected restriction. Further to this it relies on an assumption that a drought event can be assigned a probability of occurrence and associated return period despite the long acknowledged liabilities of event frequency estimation techniques (Turner et al. 2014). Especially in light of increasing climate change effects where the impacts on hydrology are likely to be non-linear and felt most at the extremes (Allen and Ingram 2002).

In response to the rising uncertainties a range of experimental frameworks and approaches are currently being developed and tested for potential use in the water industry. Recent international water resources management (WRM) literature includes a wide array of contrasting approaches for planning under “deep” uncertainty, such as: Robust Decision Making (Matrosov et al. 2013; Groves et al. 2015), Info-Gap decision theory (Korteling et al. 2013; Roach et al. 2016), Decision Scaling (Brown et al. 2012; Turner et al. 2014) and Robust Optimization (Ray et al. 2013; Kwakkel et al. 2015). Most of these approaches have been developed to evaluate the performance of a decision or strategy by calculating system robustness, which is the term commonly used to describe the degree, or percentage of plausible future conditions, under which a water supply system maintains a satisfactory level of performance. Alternative approaches incorporating flexibility analysis within the adaptive planning process are also being examined for WRM application, such as the use of Dynamic Adaptive Policy Pathways (Kwakkel et al. 2015). However, despite the widening range of approaches under development, the outputs from these methods remain highly dependent on how the water resource system performance itself is evaluated. It is within these more practical engineering features that a wider knowledge gap is often over looked.

The more well-known performance criteria often cited within WRM literature are those of Hashimoto et al. (1982) who were among the first to purpose the use of the terms reliability, vulnerability and resilience for water resource system performance evaluation. These performance criteria, in general, refer to how likely a system is to fail (its reliability), how severe the consequences of failure might be (its vulnerability) and how quickly it can bounce back, which is the recovery from a failure (its resilience). The EBSD ‘levels of service’ method used in current UK engineering practice can be most closely equated to a performance criterion of reliability and does not explicitly consider the resilience of the system. However, the latest investigation by the EA into WRM planning methods of the future (Environment Agency 2013), called for a review of the EBSD ‘levels of service’ method and for the advancement of incorporating more resilience into water resource system planning, indicating it will support adaptation strategies that are aimed at improving system resilience. Recent UK government reports have also emphasized resilience (Defra 2016); however, there is still no standard quantitative definition of resilience (Environment Agency 2013) and resilience remains generally poorly defined in practice to date.

The application of resilience as a criterion for measuring performance in WRM problems has been explored (Jung 2013; Linkov et al. 2014). Matrosov et al. (2012) and Paton et al. (2014) calculated resilience as the average duration of time a system is under a temporary restriction. Fowler et al. (2003) calculated it as a fraction of the total future time a system is under an unsatisfactory state. Loucks (1997) calculated it as the probability of a system recovering once it enters an unsatisfactory state. Kjeldsen and Rosbjerg (2004) calculated resilience in three alternative ways: the inverse of the mean value of the time the system spends in an unsatisfactory state, the maximum duration of an unsatisfactory state and the duration of the 90th fractile of observed unsatisfactory periods. They concluded that the maximum duration metric provided the most accurate and comprehensible estimation of performance. A direct maximum duration calculation was also the resilience metric of choice by Moy et al. (1986) who selected it to enable and simplify the quantification of resilience and its incorporation into a mathematical programming model. Kundzewicz and Kindler (1995) argued that a resilience definition based on a maximum value is more useful than one based on a mean, as the presence of small inconsequential events can lower the mean value and present an inaccurate picture of actual overall system performance. Using resilience as a performance criterion has also been investigated within several other areas of human, social and ecological systems science, from natural resource investigations (Tompkins and Adger 2004) to coral reef surveys (Hughes et al. 2003), with a detailed review of cross sector resilience measures conducted by Hosseini et al. (2016). It has generally been concluded that building resilience into systems (i.e. the ability to recover quickly from detrimental periods) can be an active and effective way to cope with environmental change characterized by future uncertainties and unknowable risks.

Despite several investigations involving resilience criteria (see above), few to date have applied the metric to a complex real-world WRM adaptation case study under deep uncertainty to identify optimal adaptation strategies from a wide range of potential supply and demand intervention options. Nor has a comparative analysis been conducted with results from current engineering practice. The novelty of this study lies in the assessment of whether incorporating a duration-based metric of resilience as a quantified objective in WRM assessments, in addition to appraisals of scenario-based robustness and total costs, can improve the identification of optimal adaptation strategies, when compared with the standard UK practice of performing a single least-cost linear optimization analysis constrained to a single reliability metric. To accomplish this, a novel resilience-based top-down multi-objective optimization method for the selection of optimal water resources adaptation strategies has been developed, validated and demonstrated.

The general WRM problem addressed is first defined followed by the definitions and concepts of resilience, reliability, robustness, adaptation strategies and costs. A description of the resilience-based methodology and the water resources simulation model developed for this study are then given. The quantitative case study of Bristol Water (BW) is then presented, followed by results and discussion.

The optimal solution derived by the ‘current practice’ (CP) methodology is presented first, including calculations of the respective resilience exhibited by this strategy over varying target levels of robustness. The resilience-based methodology results are presented afterwards. Selected Pareto optimal adaptation strategies from the resilience driven optimization methodology are then compared with the CP derived solution and engineering aspects discussed.

The CP methodology derives a single optimal adaptation strategy following low-cost optimization to a target reliability of ≥92% and target robustness of 90% (see section 3.5). The adaptation strategy derived has a PV of total cost of £199 M and consists of several low-cost options to reduce water consumption and water losses and several water transfer schemes scheduled from 2015 to 2017, before construction of a large reservoir at Chew Stoke (option R18 in Table 2) in 2021. Only few options are scheduled for post 2021. The full strategy details are shown in Fig. 6.

The strategy solution derived by the CP methodology is compared with the resilience driven optimization model by calculating the resilience of this strategy solution for the same target levels of robustness applied in the resilience-based methodology. Fig. 3 displays the maximum resilience maintained by the strategy under target levels of robustness of 50, 60, 70, 80, 90 and 100% respectively. It shows that this ‘reliability’ driven strategy solution can maintain a resilience as high as 3 months for at least 80% of future supply and demand scenarios, but this resilience worsens to 10 and 22 months respectively for 90% and 100% robustness respectively.

Pareto adaptation strategies were identified by the resilience driven methodology optimized by maximizing the system resilience and minimizing the PV of the total cost of adaptation strategies. Six separate optimization runs were conducted for the following target system robustness’s: 50, 60, 70, 80, 90 and 100%. Fig. 4 presents the 3D Pareto set derived from these optimizations runs as three 2D graphs displaying: (a) resilience vs cost for varying target levels of robustness, (b) robustness vs cost for varying levels of resilience and (c) resilience vs robustness for varying strategy cost groups, before being combined as a 3D–surface in Fig. 5.

The selection of a preferable adaptation strategy can be made from Fig. 4; however, the 3D–surface provides a clearer overview of the various trade-off options and affords a decision maker more perspective about how best to satisfy the various performance criteria. An ideally located individual strategy can then be selected or a specific, more desirable, region of the surface selected for further examination of individual strategies. More specifically, the decision makers can select exactly how robust and resilient they want their system to be as well as being able to discern how moderate increases or decreases in expenditure will alter the performance of the water system. Optimization to individual target levels of performance, as is undertaken in current UK engineering practice using a cost only optimization (the EBSD approach (NERA 2002)), does not allow these observations to be made. Typically, only singular optimal solutions are derived (equivalent to identifying a single point in Fig. 4(a-c)).

The CP derived optimal strategy is compared with selected strategy solutions derived by the resilience-based methodology that exhibit similar levels of resilience / total costs in order to contrast and compare the solutions derived by each method. The strategies selected are shown on Fig. 5. They consist of: strategies R1-R6, which are selected as they exhibit the same resilience to target levels of robustness as the CP solution (i.e., from Fig. 3), and strategies A1-A4 and B1-B3 as they offer increased resilience at a high level of robustness (90% for strategies A1-A4 and 80% for strategies B1-B3) for a similar PV of total cost as the CP solution. Table 3 lists the PV of total cost of each strategy examined as well as the resilience and reliability exhibited, the respective levels of robustness and the average resilience and average reliability recorded across all future scenarios examined.

Comparing the CP optimal strategy with the R1-R6 strategies in Table 3 shows that, for a lower PV of total cost, solutions are generated with the same resilience as the CP strategy for the varying target levels of robustness. For example, strategy R3 has the matching resilience of 3 months over 80% of future scenarios whilst costing approximately £25 M less than the CP strategy. The trade-off is a slight decrease in reliability of water supply, with strategy R3 maintaining a reliability of 88% over 90% of future supply/demand scenarios as opposed to 92% in the case of the CP strategy. Strategy A1, the solution of most similar total cost to the CP solution produced a more resilient system, with 90% of future scenarios now maintaining a resilience of 6 months, in contrast to the 10 months exhibited by the CP solution. The trade-off again is a moderate reduction in reliability, with a reliability of 92% being maintained over 81% of future scenarios, which falls to 88% over the remaining 9% of future scenarios within the 90% target robustness region. This demonstrates that the resilience driven methodology has identified an adaptation strategy that provides a much more resilient, but marginally less reliable system. Strategy solutions A2, A3 and B1 can further increase the resilience of the system for around 5% increase in overall total costs. Strategy solutions A4, B2 and B3 increase both the resilience and reliability of the system but for increased overall costs. These trade-offs can only be identified from the resilience-based methodology as opposed to current practice, whereby singular optimal solutions to fewer objectives are derived. If the priority design criterion for a water supply system is to maintain high reliability then this could be set as a constraint and still maintained at a high robustness. However, the benefit of the resilience-based methodology is it allows a more resilient system to then be identified in addition to high reliability, albeit at a potentially increased PV of total cost.

Fig. 6 lists the individual intervention components for each analysed strategy and their time of implementation within the 25-year planning horizon (codes for individual intervention options located in Table 2). It shows that the CP reliability driven strategy solution includes a greater number of low cost intervention options early in the planning horizon (2015) with the costliest intervention option (R18 – a new reservoir at Chew stoke) not implemented until 2021. This strategy also includes no interventions later in the planning horizon (2029–2039), implying that a number of interventions selected early on in the horizon greatly improves system reliability. Opposite of this, the alternative strategies derived by the resilience driven methodology recommend a high cost intervention early in the planning horizon (either R4 – a reservoir at Cheddar, R18 or, for the most resilient strategy (B3), R3 – a small desalination plant), before distributing a number of lower cost interventions over the remaining planning horizon, right up to 2039. This suggests larger investment early in the planning horizon as well as regular smaller water resource additions to the system increases overall system resilience, as the duration as well as frequency of severe drought periods are projected to increase over time due to climate change.

Fig. 7 demonstrates the system capacity increases (water supply capacity added to the system) provided by the CP strategy and two similarly priced strategies A1 and B1, over the 25-year planning horizon. It highlights how the outputs from the ‘levels of service’ method and the resilience driven method differ considerably in the size and timing of intervention options recommended.

The results obtained here demonstrate how simplifying a planning approach to optimize to a single criterion (i.e., reliability of supply) does not provide solutions that perform optimally across alternative criteria. The methodology proposed here produced a wide range of Pareto optimal strategies to the performance indicators of resilience, robustness and cost and allows a decision maker to select a strategy based on their final preferred trade-off across these criteria.

The variation in strategy solutions derived in this study highlights that resilience and reliability lead to differently designed systems and therefore by considering both performance indicators it may be possible to derive a solution that performs well across both metrics (see Fig. 7). Assessing resilience also increases the capability to attach economic value to the cost of water restriction periods, as a duration of deficit is more easily quantifiable than a frequency-based approach. Water planners and policy makers can more easily attach specific social, environmental and economic costs/risks, to a known duration of time rather than to a more abstract frequency of unknown events.

The detailed analysis into the sequencing of intervention options over the planning horizon and the direct effect the sequencing has on the resilience/reliability of the water system was only possible due to the utilization of the dynamic model developed in this study to simulate the monthly supply-demand balance. This highlights the additional information provided by a simulation-based approach to water resources adaptation assessments and adds further research fuel to the growing international support to move to more simulation-based assessments when dealing with deep uncertainties in water resources management.

This paper has presented a comparative assessment of a new resilience-based methodology for WRM planning that optimizes for resilience and cost for a given target level of robustness, with that of a more conventional engineering approach used in the UK.

The results obtained in the Bristol Water case study demonstrate that the new resilience-based approach for WRM planning improves on current key UK industry planning issues by: (a) increasing the transparency of adaptation strategy assessment processes and (b) improving the output information available to decision makers. The resilience-based methodology generated a 3D surface of Pareto-optimal strategies providing decision makers with a more complete trade-off picture of what different planning strategies can achieve in terms of system performance benefits and related costs thus enabling them to make better informed decisions.

In addition to above observations, a comparison of the new methodology with the current UK planning practice on the same case study resulted in further observations as follows: